六单元圆转方塞式喷管冷流试验与分析

对六单元圆转方塞式喷管的性能以及内喷管间隙与塞锥侧板的影响情况进行了冷流试验,利用纹影显示技术得到了地面、中空和高空三种工作高度下的流场结构。结合塞锥表面压强的计算结果和试验效率曲线,分析了内喷管单元间隙和塞锥侧板对性能的影响,单元之间有间隙和无塞锥侧板均会一定程度地降低喷管性能。结果表明,从地面到高空,六单元圆转方塞式喷管试验模型具有一定的高度补偿能力,其中无单元间隙有塞锥侧板的模型的推力系数效率在0.90~0.95之间变化。但设计高度下的效率低于理论预期值,其性能损失主要是圆转方内喷管型面、塞锥截短、多单元出口激波、底部不封闭等原因造成的。     

TFM imaging of aeroengine casing ring forgings with curved surfaces using acoustic field threshold segmentation and vector coherence factor

The aeroengine casing ring forgings have complex cross-section shapes, when the conventional ultrasonic or phased array is applied to detect such curved surfaces, the inspection images always have low resolution and even artifacts due to the distortion of the wave beam. In this article, taking a type of aeroengine casing ring forging as an example, the Total Focusing Method (TFM) algorithms for curved surfaces are investigated. First, the Acoustic Field Threshold Segmentation (AFTS) algorithm is proposed to reduce background noise and data calculation. Furthermore, the Vector Coherence Factor (VCF) is adopted to improve the lateral resolution of the TFM imaging. Finally, a series of 0.8 mm diameter Side-Drilled Holes (SDHs) are machined below convex and concave surfaces of the specimen. The quantitative comparison of the detection images using the conventional TFM, AFTS-TFM, VCF-TFM, and AFTS-VCF-TFM is implemented in terms of data volume, imaging Signal-to-Noise Ratio (SNR), and defect echo width. The results show that compared with conventional TFM, the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39% and 73.40%, respectively. Moreover, the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB, while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.     

S型收缩流道对涡轮通流能力影响的研究

为进一步提高航空发动机涡轮通流能力，以小型跨声速涡轮为原型，研究了静子S型收缩流道对涡轮通流能力的影响。将S型流道直线段长度和收缩段内外圆半径比作为流道造型参数，分别针对原型涡轮静子内外端壁进行调整造型，得到一系列不同参数组合的S型内外端壁涡轮算例。保持膨胀比为设计值不变，利用CFD软件对原型和S型流道涡轮进行设计点模拟分析。结果表明，S型流道涡轮流量提升的原理在于增大的静子喉道面积。在相同造型参数下，S型外端壁涡轮的静子叶根损失被有效降低，流量提升明显，且流量高于S型内端壁涡轮1%左右，但由于最大外径增大使其质量通量提升效果减弱；与之相反，S型内端壁涡轮的质量通量提升明显，且高于S型外端壁涡轮3%左右。从提升涡轮质量通量并保证效率不低于原型的角度看，S型外端壁造型参数选取范围更广。     

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.